8 ways an enterprise data strategy enables big data analytics

By Carol Newcomb, Senior Data Management Consultant at SAS

Whether you’re a mom-and-pop donut shop or a behemoth international enterprise, you rely on data to make better decisions. Data is both collected and created through day-to-day transactions with your customers. It reflects their purchase decisions and preferences, how frequently they shop and even the prices they’re willing to pay.

These days there’s unprecedented focus on managing big data (which includes structured and unstructured data characterized by huge volume, velocity and variety), or developing data lakes to store these massive quantities of data. The reason? Big data could provide even better insights into what your customers are likely to want now and in the future – be it medical treatments, a pair of jeans or the latest high-tech smartwatch.

Do you have an enterprise data strategy in place to guide the way you manage this invaluable data?

Putting together an enterprise data strategy (one time and ongoing) should be a fundamental responsibility of any organization that’s serious about using data to provide insights and direction.

I want big data. How do I get there?

There’s no doubt that big data can enrich your analytics potential. That is, assuming you have tools and techniques sophisticated enough to handle these massive data sets. Consider the impact of bringing such unwieldy and truly different data sets into your current IT shop.

As sexy as the concept of big data is, and as appealing as it may seem, not having a solid enterprise data strategy in place to manage your entire inventory of data is bound to lead to risks in data governance and data management. Establishing an enterprise data strategy will help control any vulnerability your organization may currently have relative to data. And it will help you better manage big data once you introduce it into your analytics shop.

What is an enterprise data strategy?

An enterprise data strategy is the comprehensive vision and road map for an organization’s potential to harness data-dependent capabilities. It represents the umbrella for all domain-specific strategies, such as master data management, business intelligence, big data and so forth.

A good enterprise data strategy is:

Practical (easy for the organization to follow when conducting daily activities).

Relevant (contextual to the organization, not generic).

Evolutionary (expected to change on a regular basis).

Connected/integrated (with everything that comes after it or from it).

Helps set priorities with existing data source. The first step in designing an enterprise data strategy is to collect an inventory of all data sources, applications and data owners. This step illustrates the scope and complexity of your data universe and provides the basis for decision making. It also demonstrates – to executives and those responsible for managing the data life cycle – where the gaps and competing priorities for resources exist.

Rationalizes logical and physical data architecture. The inventory should enable both business and technical conversations about the relationships between data domains and potential conflicts in definitions/terms. The result should be a logical enterprise architecture that both sides of the enterprise understand and maintain.

Provides a road map to phase out legacy systems. Your data inventory should describe the applications and platforms where data is collected and maintained. It should help you understand the capabilities of your systems, the amount of effort involved in sustaining daily operations and opportunities to modernize across platforms. Use the inventory to develop a road map and strategy for modernizing to anticipate new big data sources and desired analytics capabilities.

Improves the effectiveness of data quality processes. A robust enterprise data strategy will illustrate the data touch points for data quality monitoring and correction processes. This may include data integration points and areas for active data stewardship intervention. Use this tool to reduce inconsistencies, redundancies or gaps in data quality activities.

Requires you to rethink the data you collect, the value and the risks. Data introduces both value and risk to any organization. There are legal discovery issues to be aware of, and sharing, reporting, storing or archiving data may introduce vulnerability to regulatory initiatives. Use this tool to assess the risk your data exposes you to before you start to ramp up for new big data sources.

Avoids the burden (and hardware/storage costs) of unnecessary data. Working through an enterprise data strategy should make your enterprise more aware of the total amount of data collected and stored. Part of this awareness will come from documenting key data life cycles, understanding how much data persists in different applications and determining how long the data is considered viable. What’s the plan for big data? How does this fit with existing data retirement practices? What are the associated costs?

Establishes decision-making authority for data governance and data management. A thorough analysis of your existing data universe should include an assessment of accountability and ownership for each data source and application. This is a critical part of an enterprise data strategy. Who will be responsible for big data? How will data quality decisions be handled? Find out where accountability exists today, and where there are gaps. Establish the mechanisms for accountability through your data stewardship and data governance activities, and shore up areas that need improvement. Then consider the stewardship demands of big data.

Anticipates the true benefits of big data to enrich existing data. Now that you have a robust enterprise data strategy for the current state of affairs, you can begin to plan for where you should introduce big data sources to supplement analytics capabilities versus where they would introduce risk. You’ll need not only the platforms and data management resources to handle volumes of data; you’ll also need the processes and human capital in place to be accountable for questions that will inevitably arise with entirely new types of data.

Get serious about your enterprise data strategy

Putting together an enterprise data strategy (one time and ongoing) should be a fundamental responsibility of any organization that’s serious about using data to provide insights and direction. Even before introducing big data into a mature, sophisticated IT shop, you should anticipate the fact that big data sources are fundamentally different. The differences will require careful planning and staffing to ensure you’re prepared for the impact and potential risks involved as you learn to use big data effectively.

Carol Newcomb is a Data Management Consultant for SAS with more than 25 years’ experience in information management. She specializes in the design and implementation of data governance programs and data strategy for a broad range of industries. She is author of the SAS e-book When Bad Data Happens to Good Companies and has written numerous blogs and white papers, including Implementing Data Governance in Complex Health Care Organizations.